Editorial: Special issue on recent progress in autonomous machine learning

Autonomous Machine Learning (AML) refers to a learning system having flexible characteristic to evolve both its network structure and parameters on the fly. It is capable of initiating its learning process from scratch with/without a predefined network structure while its knowledge base is automatically constructed in real-time. AML is built upon two fundamental principles: one-pass learning strategy and self-evolving network structure. The former one reflects a situation where a data point is directly discarded once learned to assure bounded memory and computational burdens while the latter lies in the self-reconfiguration aptitude of AML where its network size can increase or reduce in respect to varying data distributions. AMLs have been proven to be useful in handling real-time data streams where a learning system confronts never-ending information flow which does not follow static or predictable data distributions rather drifting overtime with different types, magnitudes and types. Variants of AMLs are capable of quickly reacting to those drifting distributions regardless of how slow, fast, sudden, gradual, cyclic changing distributions might be while retaining computationally light characteristics. In addition, the AMLs have grown into various application domains not only limited to regression, classification, clustering but also control and reinforcement learning. In a nutshell, it is enabled by the fact that AMLs aim to balance between stability and plasticity of a learning system.